Hui Zhang
3 years ago
parent
3bd87bc379
commit
b9790d03f2
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# network architecture
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model:
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# encoder related
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encoder: conformer
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encoder_conf:
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output_size: 512 # dimension of attention
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attention_heads: 8
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: True
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use_cnn_module: True
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cnn_module_kernel: 15
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cnn_module_norm: layer_norm
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activation_type: swish
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pos_enc_layer_type: rel_pos
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selfattention_layer_type: rel_selfattn
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 8
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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ctc_dropoutrate: 0.0
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ctc_grad_norm_type: null
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# https://yaml.org/type/float.html
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data:
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test
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min_input_len: 0.1 # second
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max_input_len: 12.0 # second
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min_output_len: 1.0
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max_output_len: 400.0
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min_output_input_ratio: 0.05
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max_output_input_ratio: 10.0
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: 'char'
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spm_model_prefix: ''
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augmentation_config: conf/preprocess.yaml
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batch_size: 64
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raw_wav: True # use raw_wav or kaldi feature
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spectrum_type: fbank #linear, mfcc, fbank
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feat_dim: 80
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delta_delta: False
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dither: 1.0
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target_sample_rate: 16000
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max_freq: None
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n_fft: None
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stride_ms: 10.0
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window_ms: 25.0
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use_dB_normalization: True
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target_dB: -20
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random_seed: 0
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keep_transcription_text: False
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sortagrad: True
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shuffle_method: batch_shuffle
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num_workers: 2
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training:
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n_epoch: 240
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accum_grad: 16
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global_grad_clip: 5.0
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log_interval: 100
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checkpoint:
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kbest_n: 50
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latest_n: 5
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optim: adam
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optim_conf:
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lr: 0.001
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weight_decay: 1e-6
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 5000
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lr_decay: 1.0
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decoding:
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batch_size: 128
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error_rate_type: cer
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decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
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lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
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alpha: 2.5
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beta: 0.3
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beam_size: 10
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cutoff_prob: 1.0
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cutoff_top_n: 0
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num_proc_bsearch: 8
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ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
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decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
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# <0: for decoding, use full chunk.
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# >0: for decoding, use fixed chunk size as set.
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# 0: used for training, it's prohibited here.
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num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
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simulate_streaming: False # simulate streaming inference. Defaults to False.
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process:
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# extract kaldi fbank from PCM
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- type: fbank_kaldi
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fs: 16000
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n_mels: 80
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n_shift: 160
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win_length: 400
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dither: true
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- type: cmvn_json
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cmvn_path: data/mean_std.json
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# these three processes are a.k.a. SpecAugument
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- type: time_warp
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max_time_warp: 5
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inplace: true
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mode: PIL
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- type: freq_mask
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F: 30
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n_mask: 2
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inplace: true
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replace_with_zero: false
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- type: time_mask
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T: 40
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n_mask: 2
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inplace: true
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replace_with_zero: false
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#!/bin/bash
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# Copyright 2021 Mobvoi Inc(Author: Di Wu, Binbin Zhang)
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# NPU, ASLP Group (Author: Qijie Shao)
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stage=-1
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stop_stage=100
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# Use your own data path. You need to download the WenetSpeech dataset by yourself.
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wenetspeech_data_dir=./wenetspeech
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# Make sure you have 1.2T for ${shards_dir}
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shards_dir=./wenetspeech_shards
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#wenetspeech training set
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set=L
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train_set=train_`echo $set | tr 'A-Z' 'a-z'`
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dev_set=dev
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test_sets="test_net test_meeting"
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cmvn=true
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cmvn_sampling_divisor=20 # 20 means 5% of the training data to estimate cmvn
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. ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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set -u
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set -o pipefail
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mkdir -p data
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TARGET_DIR=${MAIN_ROOT}/examples/dataset
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mkdir -p ${TARGET_DIR}
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if [ ${stage} -le -2 ] && [ ${stop_stage} -ge -2 ]; then
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# download data
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echo "Please follow https://github.com/wenet-e2e/WenetSpeech to download the data."
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exit 0;
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "Data preparation"
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local/wenetspeech_data_prep.sh \
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--train-subset $set \
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$wenetspeech_data_dir \
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data || exit 1;
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fi
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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# generate manifests
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python3 ${TARGET_DIR}/aishell/aishell.py \
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--manifest_prefix="data/manifest" \
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--target_dir="${TARGET_DIR}/aishell"
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if [ $? -ne 0 ]; then
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echo "Prepare Aishell failed. Terminated."
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exit 1
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fi
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for dataset in train dev test; do
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mv data/manifest.${dataset} data/manifest.${dataset}.raw
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done
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# compute mean and stddev for normalizer
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if $cmvn; then
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full_size=`cat data/${train_set}/wav.scp | wc -l`
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sampling_size=$((full_size / cmvn_sampling_divisor))
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shuf -n $sampling_size data/$train_set/wav.scp \
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> data/$train_set/wav.scp.sampled
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num_workers=$(nproc)
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python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
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--manifest_path="data/manifest.train.raw" \
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--spectrum_type="fbank" \
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--feat_dim=80 \
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--delta_delta=false \
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--stride_ms=10 \
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--window_ms=25 \
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--sample_rate=16000 \
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--use_dB_normalization=False \
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--num_samples=-1 \
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--num_workers=${num_workers} \
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--output_path="data/mean_std.json"
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if [ $? -ne 0 ]; then
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echo "Compute mean and stddev failed. Terminated."
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exit 1
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fi
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fi
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fi
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dict=data/dict/lang_char.txt
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# download data, generate manifests
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# build vocabulary
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python3 ${MAIN_ROOT}/utils/build_vocab.py \
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--unit_type="char" \
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--count_threshold=0 \
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--vocab_path="data/vocab.txt" \
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--manifest_paths "data/manifest.train.raw"
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if [ $? -ne 0 ]; then
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echo "Build vocabulary failed. Terminated."
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exit 1
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fi
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# format manifest with tokenids, vocab size
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for dataset in train dev test; do
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{
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python3 ${MAIN_ROOT}/utils/format_data.py \
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--cmvn_path "data/mean_std.json" \
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--unit_type "char" \
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--vocab_path="data/vocab.txt" \
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--manifest_path="data/manifest.${dataset}.raw" \
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--output_path="data/manifest.${dataset}"
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if [ $? -ne 0 ]; then
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echo "Formt mnaifest failed. Terminated."
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exit 1
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fi
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} &
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done
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wait
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fi
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echo "Aishell data preparation done."
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exit 0
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@ -0,0 +1,102 @@
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# Copyright 2021 Xiaomi Corporation (Author: Yongqing Wang)
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# Mobvoi Inc(Author: Di Wu, Binbin Zhang)
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import os
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import argparse
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import json
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def get_args():
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parser = argparse.ArgumentParser(description="""
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This script is used to process raw json dataset of WenetSpeech,
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where the long wav is splitinto segments and
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data of wenet format is generated.
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""")
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parser.add_argument('input_json', help="""Input json file of WenetSpeech""")
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parser.add_argument('output_dir', help="""Output dir for prepared data""")
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args = parser.parse_args()
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return args
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def meta_analysis(input_json, output_dir):
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input_dir = os.path.dirname(input_json)
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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try:
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with open(input_json, 'r') as injson:
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json_data = json.load(injson)
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except Exception:
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sys.exit(f'Failed to load input json file: {input_json}')
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else:
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if json_data['audios'] is not None:
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with open(f'{output_dir}/text', 'w') as utt2text, \
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open(f'{output_dir}/segments', 'w') as segments, \
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open(f'{output_dir}/utt2dur', 'w') as utt2dur, \
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open(f'{output_dir}/wav.scp', 'w') as wavscp, \
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open(f'{output_dir}/utt2subsets', 'w') as utt2subsets, \
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open(f'{output_dir}/reco2dur', 'w') as reco2dur:
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for long_audio in json_data['audios']:
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try:
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long_audio_path = os.path.realpath(
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os.path.join(input_dir, long_audio['path']))
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aid = long_audio['aid']
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segments_lists = long_audio['segments']
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duration = long_audio['duration']
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assert (os.path.exists(long_audio_path))
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except AssertionError:
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print(f'''Warning: {aid} something is wrong,
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maybe AssertionError, skipped''')
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continue
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except Exception:
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print(f'''Warning: {aid} something is wrong, maybe the
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error path: {long_audio_path}, skipped''')
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continue
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else:
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wavscp.write(f'{aid}\t{long_audio_path}\n')
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reco2dur.write(f'{aid}\t{duration}\n')
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for segment_file in segments_lists:
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try:
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sid = segment_file['sid']
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start_time = segment_file['begin_time']
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end_time = segment_file['end_time']
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dur = end_time - start_time
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text = segment_file['text']
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segment_subsets = segment_file["subsets"]
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except Exception:
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print(f'''Warning: {segment_file} something
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is wrong, skipped''')
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continue
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else:
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utt2text.write(f'{sid}\t{text}\n')
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segments.write(
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f'{sid}\t{aid}\t{start_time}\t{end_time}\n'
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)
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utt2dur.write(f'{sid}\t{dur}\n')
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segment_sub_names = " ".join(segment_subsets)
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utt2subsets.write(
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f'{sid}\t{segment_sub_names}\n')
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def main():
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args = get_args()
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meta_analysis(args.input_json, args.output_dir)
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if __name__ == '__main__':
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main()
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@ -0,0 +1,89 @@
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# Copyright 2021 NPU, ASLP Group (Author: Qijie Shao)
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# process_opus.py: segmentation and downsampling of opus audio
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# usage: python3 process_opus.py wav.scp segments output_wav.scp
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from pydub import AudioSegment
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import sys
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import os
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def read_file(wav_scp, segments):
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wav_scp_dict = {}
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with open(wav_scp, 'r', encoding='UTF-8') as fin:
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for line_str in fin:
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wav_id, path = line_str.strip().split()
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wav_scp_dict[wav_id] = path
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utt_list = []
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seg_path_list = []
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start_time_list = []
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end_time_list = []
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with open(segments, 'r', encoding='UTF-8') as fin:
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for line_str in fin:
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arr = line_str.strip().split()
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assert len(arr) == 4
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utt_list.append(arr[0])
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seg_path_list.append(wav_scp_dict[arr[1]])
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start_time_list.append(float(arr[2]))
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end_time_list.append(float(arr[3]))
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return utt_list, seg_path_list, start_time_list, end_time_list
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# TODO(Qijie): Fix the process logic
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def output(output_wav_scp, utt_list, seg_path_list, start_time_list,
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end_time_list):
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num_utts = len(utt_list)
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step = int(num_utts * 0.01)
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with open(output_wav_scp, 'w', encoding='UTF-8') as fout:
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previous_wav_path = ""
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for i in range(num_utts):
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utt_id = utt_list[i]
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current_wav_path = seg_path_list[i]
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output_dir = (os.path.dirname(current_wav_path)) \
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.replace("audio", 'audio_seg')
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seg_wav_path = os.path.join(output_dir, utt_id + '.wav')
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# if not os.path.exists(output_dir):
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# os.makedirs(output_dir)
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if current_wav_path != previous_wav_path:
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source_wav = AudioSegment.from_file(current_wav_path)
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previous_wav_path = current_wav_path
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start = int(start_time_list[i] * 1000)
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end = int(end_time_list[i] * 1000)
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target_audio = source_wav[start:end].set_frame_rate(16000)
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target_audio.export(seg_wav_path, format="wav")
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fout.write("{} {}\n".format(utt_id, seg_wav_path))
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if i % step == 0:
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print("seg wav finished: {}%".format(int(i / step)))
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def main():
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wav_scp = sys.argv[1]
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segments = sys.argv[2]
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output_wav_scp = sys.argv[3]
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utt_list, seg_path_list, start_time_list, end_time_list \
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= read_file(wav_scp, segments)
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output(output_wav_scp, utt_list, seg_path_list, start_time_list,
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end_time_list)
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if __name__ == '__main__':
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main()
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@ -0,0 +1 @@
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decode_modes="attention_rescoring ctc_greedy_search"
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@ -0,0 +1,135 @@
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#!/usr/bin/env bash
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||||
# Copyright 2021 Xiaomi Corporation (Author: Yongqing Wang)
|
||||
# Seasalt AI, Inc (Author: Guoguo Chen)
|
||||
# Mobvoi Inc(Author: Di Wu, Binbin Zhang)
|
||||
# NPU, ASLP Group (Author: Qijie Shao)
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
set -e
|
||||
set -o pipefail
|
||||
|
||||
stage=1
|
||||
prefix=
|
||||
train_subset=L
|
||||
|
||||
. ./tools/parse_options.sh || exit 1;
|
||||
|
||||
filter_by_id () {
|
||||
idlist=$1
|
||||
input=$2
|
||||
output=$3
|
||||
field=1
|
||||
if [ $# -eq 4 ]; then
|
||||
field=$4
|
||||
fi
|
||||
cat $input | perl -se '
|
||||
open(F, "<$idlist") || die "Could not open id-list file $idlist";
|
||||
while(<F>) {
|
||||
@A = split;
|
||||
@A>=1 || die "Invalid id-list file line $_";
|
||||
$seen{$A[0]} = 1;
|
||||
}
|
||||
while(<>) {
|
||||
@A = split;
|
||||
@A > 0 || die "Invalid file line $_";
|
||||
@A >= $field || die "Invalid file line $_";
|
||||
if ($seen{$A[$field-1]}) {
|
||||
print $_;
|
||||
}
|
||||
}' -- -idlist="$idlist" -field="$field" > $output ||\
|
||||
(echo "$0: filter_by_id() error: $input" && exit 1) || exit 1;
|
||||
}
|
||||
|
||||
subset_data_dir () {
|
||||
utt_list=$1
|
||||
src_dir=$2
|
||||
dest_dir=$3
|
||||
mkdir -p $dest_dir || exit 1;
|
||||
# wav.scp text segments utt2dur
|
||||
filter_by_id $utt_list $src_dir/utt2dur $dest_dir/utt2dur ||\
|
||||
(echo "$0: subset_data_dir() error: $src_dir/utt2dur" && exit 1) || exit 1;
|
||||
filter_by_id $utt_list $src_dir/text $dest_dir/text ||\
|
||||
(echo "$0: subset_data_dir() error: $src_dir/text" && exit 1) || exit 1;
|
||||
filter_by_id $utt_list $src_dir/segments $dest_dir/segments ||\
|
||||
(echo "$0: subset_data_dir() error: $src_dir/segments" && exit 1) || exit 1;
|
||||
awk '{print $2}' $dest_dir/segments | sort | uniq > $dest_dir/reco
|
||||
filter_by_id $dest_dir/reco $src_dir/wav.scp $dest_dir/wav.scp ||\
|
||||
(echo "$0: subset_data_dir() error: $src_dir/wav.scp" && exit 1) || exit 1;
|
||||
rm -f $dest_dir/reco
|
||||
}
|
||||
|
||||
if [ $# -ne 2 ]; then
|
||||
echo "Usage: $0 [options] <wenetspeech-dataset-dir> <data-dir>"
|
||||
echo " e.g.: $0 --train-subset L /disk1/audio_data/wenetspeech/ data/"
|
||||
echo ""
|
||||
echo "This script takes the WenetSpeech source directory, and prepares the"
|
||||
echo "WeNet format data directory."
|
||||
echo " --prefix <prefix> # Prefix for output data directory."
|
||||
echo " --stage <stage> # Processing stage."
|
||||
echo " --train-subset <L|M|S|W> # Train subset to be created."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
wenetspeech_dir=$1
|
||||
data_dir=$2
|
||||
|
||||
declare -A subsets
|
||||
subsets=(
|
||||
[L]="train_l"
|
||||
[M]="train_m"
|
||||
[S]="train_s"
|
||||
[W]="train_w"
|
||||
[DEV]="dev"
|
||||
[TEST_NET]="test_net"
|
||||
[TEST_MEETING]="test_meeting")
|
||||
|
||||
prefix=${prefix:+${prefix}_}
|
||||
|
||||
corpus_dir=$data_dir/${prefix}corpus/
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: Extract meta into $corpus_dir"
|
||||
# Sanity check.
|
||||
[ ! -f $wenetspeech_dir/WenetSpeech.json ] &&\
|
||||
echo "$0: Please download $wenetspeech_dir/WenetSpeech.json!" && exit 1;
|
||||
[ ! -d $wenetspeech_dir/audio ] &&\
|
||||
echo "$0: Please download $wenetspeech_dir/audio!" && exit 1;
|
||||
|
||||
[ ! -d $corpus_dir ] && mkdir -p $corpus_dir
|
||||
|
||||
# Files to be created:
|
||||
# wav.scp text segments utt2dur
|
||||
python3 local/extract_meta.py \
|
||||
$wenetspeech_dir/WenetSpeech.json $corpus_dir || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: Split data to train, dev, test_net, and test_meeting"
|
||||
[ ! -f $corpus_dir/utt2subsets ] &&\
|
||||
echo "$0: No such file $corpus_dir/utt2subsets!" && exit 1;
|
||||
for label in $train_subset DEV TEST_NET TEST_MEETING; do
|
||||
if [ ! ${subsets[$label]+set} ]; then
|
||||
echo "$0: Subset $label is not defined in WenetSpeech.json." && exit 1;
|
||||
fi
|
||||
subset=${subsets[$label]}
|
||||
[ ! -d $data_dir/${prefix}$subset ] && mkdir -p $data_dir/${prefix}$subset
|
||||
cat $corpus_dir/utt2subsets | \
|
||||
awk -v s=$label '{for (i=2;i<=NF;i++) if($i==s) print $0;}' \
|
||||
> $corpus_dir/${prefix}${subset}_utt_list|| exit 1;
|
||||
subset_data_dir $corpus_dir/${prefix}${subset}_utt_list \
|
||||
$corpus_dir $data_dir/${prefix}$subset || exit 1;
|
||||
done
|
||||
fi
|
||||
|
||||
echo "$0: Done"
|
||||
@ -0,0 +1,15 @@
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
|
||||
|
||||
# model exp
|
||||
MODEL=u2
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin
|
||||
@ -0,0 +1,55 @@
|
||||
#!/bin/bash
|
||||
|
||||
. path.sh || exit 1;
|
||||
set -e
|
||||
|
||||
gpus=0,1,2,3,4,5,6,7
|
||||
stage=0
|
||||
stop_stage=100
|
||||
conf_path=conf/conformer.yaml
|
||||
|
||||
average_checkpoint=true
|
||||
avg_num=10
|
||||
|
||||
. ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
|
||||
|
||||
avg_ckpt=avg_${avg_num}
|
||||
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
|
||||
echo "checkpoint name ${ckpt}"
|
||||
|
||||
audio_file="data/tmp.wav"
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
bash ./local/data.sh || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `exp` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# avg n best model
|
||||
avg.sh best exp/${ckpt}/checkpoints ${avg_num}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# test ckpt avg_n
|
||||
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
# ctc alignment of test data
|
||||
CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
|
||||
# export ckpt avg_n
|
||||
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
|
||||
# test a single .wav file
|
||||
CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
|
||||
fi
|
||||
Loading…
Reference in new issue